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From Citation Selection to Citation Absorption: A Measurement Framework for Generative Engine Optimization Across AI Search Platforms

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Authors
Zhang Kai, He Xinyue, Yao Jingang
Venue
arXiv — preprint, not yet peer-reviewed
Published
2026-04-29
ID
arXiv:2604.25707

How to cite it

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APA
Kai, Z., Xinyue, H., & Jingang, Y. (2026). From Citation Selection to Citation Absorption: A Measurement Framework for Generative Engine Optimization Across AI Search Platforms. arXiv — preprint, not yet peer-reviewed. https://arxiv.org/abs/2604.25707
MLA
Kai, Zhang, et al. "From Citation Selection to Citation Absorption: A Measurement Framework for Generative Engine Optimization Across AI Search Platforms." arXiv — preprint, not yet peer-reviewed, 2026, https://arxiv.org/abs/2604.25707.
BibTeX
@article{kai2026from,
  title = {From Citation Selection to Citation Absorption: A Measurement Framework for Generative Engine Optimization Across AI Search Platforms},
  author = {Zhang Kai and He Xinyue and Yao Jingang},
  journal = {arXiv — preprint, not yet peer-reviewed},
  year = {2026},
  url = {https://arxiv.org/abs/2604.25707},
}

What it is

Preprint, not yet peer-reviewed. Zhang Kai, He Xinyue and Yao Jingang propose separating source selection from how much a cited page contributes to an AI-generated answer. Their analysis of 602 prompts compares ChatGPT, Google AI Overview/Gemini and Perplexity; descriptive associations do not establish a causal recipe for ranking or visibility.

Topics

Added Nov 13, 2026 · 0 opens